The present invention relates to optimization of wireless video sensor networks. More specifically, the present invention relates to optimization of wireless video sensor networks with limited resources to ensure optimal performance for a visual cognition problem at hand. The phrase visual cognition is meant to include but not limited to problems spanning from 3D model reconstruction to higher level cognitive tasks entailed in visual intelligence, surveillance, reconnaissance and other higher level cognition activities that the network is tasked to perform—for example, multi-camera tracking of targets.
Wireless Video Sensor Networks (WSN), designed for gathering real-time visual intelligence, are of increasing importance for a number of areas of application, such as disaster recovery and rescue operations, law enforcement, and, most notably, military operations. Generally, WSN refers to any type of computer network comprising a plurality of signal sensors that is not connected by cables of any kind and that may typically cover large areas, such as cities, regions, and continents. A typical WSN contains one or more servers, base station gateways, access points, wireless bridging relays, and sensor nodes. While being capable of providing its users with intelligent video analytics support in service of the specified visual cognition task, the challenge for WSN is to be able to self-organize, self manage its energy resources, and re-configure to respond to dynamically changing conditions.